With the increasing attention toward the global climate change and environmental consciousness, green energy, such as solar power, wind power, wave power, geothermal power, hydrogen energy or biomass energy, is becoming a focal point for industries all over the world since it can be extracted, generated, and/or consumed without any significant negative impact to the environment. Among which, wind power is the one most likely to become the alternative energy source of the future since its development had exceeded others.
It is noted that wind turbine is the device most commonly used today for harnessing and converting wind power into electricity, Nevertheless, while operating under spatially and temporally heterogeneous and unstable wind field, not only the wind turbine might not be able to produce electricity in direct proportion with the wind speed and the amount of wind being received thereby, but also the sudden disruption of violent gusts may cause damages to the key components of the wind turbine, such as the gear box and generator. Thus, it is important to be able to predict and estimate the efficiency as well as the lifespan of the wind turbines so as to optimize its performance.
There are two types of lifespan estimation method that are most often used, which are a theoretical lifespan analysis and estimation for key components of wind turbines; and a machinery state-of-health evaluation and identification based upon the experience of field maintenance staff or experts.
In the theoretical lifespan analysis and estimation method, the lifespan of any key component is estimated and anticipated using a calculation based upon a service life expectancy evaluation relating to the material of the key component. Nevertheless, in order to obtain such theoretical maximum service life estimation for the key component, such as gears and bearings, the calculation is mostly based upon how good the ability of the material that is used to make the key component can resist fatigue failure. Therefore, it is required to have a plurality of basic parameters, such as material characteristics, operation modes or working environment, to be defined before the calculation for obtaining such theoretical maximum service life can be performed. However, any error in the definition of any such basic parameter can severely affect the accuracy of the resulting theoretical maximum service life estimation. Moreover, since the environment parameters for characterizing an actual working environment are generally are non-linearly distributed, the definitions of such environment parameters can be very difficult to obtain.
In the machinery state-of-health evaluation and identification based upon the experience of field maintenance staff or experts, since the machinery used in the field can vary with the change of working environment and the field maintenance staff can performed the estimation only based upon their own working experience and the current working status of the machinery, the accuracy of the estimation is severely dependent upon how experienced the field maintenance staff is.
To sum up, as one of the two most commonly used methods for efficiency and service life estimation can only be performed after a plurality of basic parameters are obtained and defined exactly corresponding to the actual working environment, but that can be a very difficult task, while the accuracy of another method is solely depending upon the experience of its field maintenance staff, not only the accuracy is in question, but also the procedures for performing of the aforesaid methods can not be standardized as they can be heavily depending on individual experience. Thus, there still are much to be improved in the aforesaid efficiency and service life estimation methods.